IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_11479.html
   My bibliography  Save this paper

Generative AI and the Nature of Work

Author

Listed:
  • Manuel Hoffmann
  • Sam Boysel
  • Frank Nagle
  • Sida Peng
  • Kevin Xu

Abstract

Recent advances in artificial intelligence (AI) technology demonstrate considerable potential to complement human capital intensive activities. While an emerging literature documents wide-ranging productivity effects of AI, relatively little attention has been paid to how AI might change the nature of work itself. How do individuals, especially those in the knowledge economy, adjust how they work when they start using AI? Using the setting of open source software, we study individual level effects that AI has on task allocation. We exploit a natural experiment arising from the deployment of GitHub Copilot, a generative AI code completion tool for software developers. Leveraging millions of work activities over a two year period, we use a program eligibility threshold to investigate the impact of AI technology on the task allocation of software developers within a quasi-experimental regression discontinuity design. We find that having access to Copilot induces such individuals to shift task allocation towards their core work of coding activities and away from non-core project management activities. We identify two underlying mechanisms driving this shift - an increase in autonomous rather than collaborative work, and an increase in exploration activities rather than exploitation. The main effects are greater for individuals with relatively lower ability. Overall, our estimates point towards a large potential for AI to transform work processes and to potentially flatten organizational hierarchies in the knowledge economy.

Suggested Citation

  • Manuel Hoffmann & Sam Boysel & Frank Nagle & Sida Peng & Kevin Xu, 2024. "Generative AI and the Nature of Work," CESifo Working Paper Series 11479, CESifo.
  • Handle: RePEc:ces:ceswps:_11479
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11479.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicholas Crafts, 2021. "Artificial intelligence as a general-purpose technology: an historical perspective," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 521-536.
    2. David Autor, 2024. "Applying AI to Rebuild Middle Class Jobs," NBER Working Papers 32140, National Bureau of Economic Research, Inc.
    3. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022. "Artificial Intelligence and Jobs: Evidence from Online Vacancies," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
    4. Carol Corrado & Jonathan Haskel & Cecilia Jona-Lasinio, 2021. "Artificial intelligence and productivity: an intangible assets approach," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 435-458.
    5. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Minniti, Antonio & Prettner, Klaus & Venturini, Francesco, 2024. "Unslicing the pie: AI innovation and the labor share in European regions," Department of Economics Working Paper Series 369, WU Vienna University of Economics and Business.
    2. Saam Marianne, 2024. "The Impact of Artificial Intelligence on Productivity and Employment – How Can We Assess It and What Can We Observe?," Intereconomics: Review of European Economic Policy, Sciendo, vol. 59(1), pages 22-27, February.
    3. Flavio Calvino & Chiara Criscuolo & Luca Fontanelli & Lionel Nesta & Elena Verdolini, 2024. "The role of human capital for AI adoption: Evidence from French firms," CEP Discussion Papers dp2055, Centre for Economic Performance, LSE.
    4. Enrico Maria Fenoaltea & Dario Mazzilli & Aurelio Patelli & Angelica Sbardella & Andrea Tacchella & Andrea Zaccaria & Marco Trombetti & Luciano Pietronero, 2024. "Follow the money: a startup-based measure of AI exposure across occupations, industries and regions," Papers 2412.04924, arXiv.org, revised Dec 2024.
    5. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    6. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    7. Alexander Bick & Adam Blandin & David Deming, 2023. "The Rapid Adoption of Generative AI," On the Economy 98843, Federal Reserve Bank of St. Louis.
    8. Samuel Muehlemann, 2024. "AI Adoption and Workplace Training," Economics of Education Working Paper Series 0232, University of Zurich, Department of Business Administration (IBW).
    9. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, Institute of Labor Economics (IZA).
    10. Mühlemann, Samuel, 2024. "AI Adoption and Workplace Training," IZA Discussion Papers 17367, Institute of Labor Economics (IZA).
    11. Raluca-Florentina Cretu & Daniela Tutui & Viorel-Costin Banta & Elena Claudia Serban & Laura - Eugenia - Lavinia Barna & Romeo-Catalin Cretu, 2024. "Effects of Artificial Intelligence-Based Technologies Implementation s on the Skills Needed in the Automotive Industry A Bibliometric Analysis," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 801-801, August.
    12. Shigeru Fujita & Madison Perry, 2024. "Nonworking Parents or Hungry Children," Economic Insights, Federal Reserve Bank of Philadelphia, vol. 9(4), pages 2-9, December.
    13. Tamay Besiroglu & Nicholas Emery-Xu & Neil Thompson, 2022. "Economic impacts of AI-augmented R&D," Papers 2212.08198, arXiv.org, revised Jan 2023.
    14. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
    15. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    16. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.
    17. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    18. Giovanni Dosi & Marcelo C. Pereira & Andrea Roventini & Maria Enrica Virgillito, 2024. "The political economy of complex evolving systems: the case of declining unionization and rising inequalities," LEM Papers Series 2024/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    19. Engberg, Erik & Görg, Holger & Lodefalk, Magnus & Javed, Farrukh & Längkvist, Martin & Monteiro, Natália & Kyvik Nordås, Hildegunn & Pulito, Giuseppe & Schroeder, Sarah & Tang, Aili, 2023. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," Ratio Working Papers 370, The Ratio Institute.
    20. Bloom, Nicholas & Davis, Steven J. & Hansen, Stephen & Lambert, Peter John & Sadun, Raffaella & Taska, Bledi, 2023. "Remote work across jobs, companies and space," LSE Research Online Documents on Economics 121302, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    generative artificial intelligence; digital work; open source software; knowledge economy;
    All these keywords.

    JEL classification:

    • H40 - Public Economics - - Publicly Provided Goods - - - General
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • J00 - Labor and Demographic Economics - - General - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_11479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.